WO2014205585A1 - Procédé et système d'optimisation de localisation de centres de données ou de points d'occupation et composants logiciels dans un réseau informatique en nuage utilisant un algorithme de recherche tabou - Google Patents

Procédé et système d'optimisation de localisation de centres de données ou de points d'occupation et composants logiciels dans un réseau informatique en nuage utilisant un algorithme de recherche tabou Download PDF

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WO2014205585A1
WO2014205585A1 PCT/CA2014/050623 CA2014050623W WO2014205585A1 WO 2014205585 A1 WO2014205585 A1 WO 2014205585A1 CA 2014050623 W CA2014050623 W CA 2014050623W WO 2014205585 A1 WO2014205585 A1 WO 2014205585A1
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network
cost
data center
data centers
data
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Federico LARUMBE
Brunilde SANSO
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Polyvalor, Société En Commandite
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/12—Discovery or management of network topologies
    • H04L41/122—Discovery or management of network topologies of virtualised topologies, e.g. software-defined networks [SDN] or network function virtualisation [NFV]

Definitions

  • the present invention relates to a method and system for optimizing the location of data centers or points of presence (PoPs) and software components in cloud computing networks, particularly data center locations supplied by green energy sources. It also finds the optimal dimension of the telecommunication backbone linking the data centers, the number of required servers in each data center and the routing.
  • PoPs points of presence
  • Service providers may own servers and data centers or, alternatively, may contract infrastructure providers that use economies of scale to offer access to servers as a service in the cloud computing model, i.e., Infrastructure as a Service (laaS).
  • laaS Infrastructure as a Service
  • a fundamental metric that defines the quality of service is the delay of the information as it travels between the user computers and the servers, and between the servers themselves.
  • Figure 1 is a block diagram of a network and application layers, according to a preferred embodiment of the present invention.
  • Figure 2 is a block diagram of a network layer of a greedy solution for an example of a web engine
  • Figure 3 is a network topology diagram with 10 cities and 10 potential data center locations
  • Figure 4 is a network topology diagram corresponding to solution A : Delay minimization
  • Figure 5 is a network topology diagram corresponding to solution B : pollution minimization
  • Figure 6 is a network topology diagram corresponding to solution C : cost minimization
  • Figure 7 is graph of a tabu search vs greedy heuristic for large cases and cost priorities.
  • Figure 8 is a block diagram illustrating a method for locating data centers or points of presence and software components in a cloud computing network, according to a preferred embodiment of the present invention.
  • data center as a facility used to house computer systems and associated components, such as telecommunications and storage systems. It may include redundant or backup power supplies, redundant data communications connections, environmental controls (e.g., air conditioning, fire suppression) and various security devices.
  • Point of Presence As an access point to the Internet. It is a physical location that may house servers, routers, ATM switches and digital/analog call aggregators. Given that servers and software components can be hosted in either points of presence or data centers, we use the term data center to denote both concepts. That is, Point of Presence locations are included in the set of potential data center locations.
  • the criteria to choose the optimal solutions in our framework are embedded in a multi-objective function that allows planners to weight each attribute according to their priorities.
  • the objective function is composed of the following metrics: traffic delay, energy consumption, C0 2 emissions, traffic cost, server cost, data center capital expenditures (CAPEX), and data center operational expenditures (OPEX).
  • the proposed problem is formalized as a mixed linear-integer programming model and solved, first with AMPL-Cplex, and then with a very efficient tabu search heuristic. Because there are many integer decision variables, the AMPL-Cplex model ran for 58 minutes for a small network with 24 potential data center locations. Furthermore, the cases used for this study were networks with up to 500 access nodes and 1 ,000 potential data center locations; in fact, one of the largest cloud networks has in the order of 1 ,000 small data centers [5]. Thus, a specific heuristic is required to design this type of network. We show that the tabu search algorithm presented in this application achieved very small optimality gaps, and the execution time ranged from some milliseconds to less than 10 minutes.
  • the framework presented in this application helps to understand each aspect of a cloud network in a formal way, and multiple actors may be interested in having optimal solutions to this problem.
  • One actor may be an infrastructure provider that needs to deploy or extend a data center network used by service providers.
  • Another actor may be a service provider that needs to choose the cloud data centers to deploy global distributed applications and to decide the number of servers to host in each one of them.
  • Yet another actor may be organizations with their private clouds who want to solve the whole network design in an integrated and optimal way.
  • Section 2 presents a literature review of related problems and models.
  • Section 3 describes the problem and proposes a Mixed Integer Linear Programming (MILP) model.
  • Section 4 specifies the tabu search heuristic that was developed to solve large problems.
  • Section 5 presents a case study of a search engine application that requires data centers and software components to be placed around the world.
  • Section 6 shows optimal solutions for the case study and analyzes how the multiple objectives interact. This section also shows the optimality gap of the tabu search heuristic and the execution time depending on the instance sizes.
  • [0027] With respect to the application location, [1 1] was the first to model the software component placement considering the information traffic between the components.
  • the proposed resolution method based on a maximum flow algorithm was restricted to the case of two processors.
  • [12] served web requests from multiple data centers to minimize the total energy cost by changing the workload assignment, depending on the energy price, and guaranteeing the quality of service. The problem was linearized and then reduced to a minimum cost flow problem.
  • [3] studied dynamic provisioning of applications from multiple cloud vendors and did a performance study with the CloudSim toolkit. That paper shows that the cloud federation model yields important benefits in quality of service and costs.
  • a related problem is virtual network embedding, which maps a virtual network with virtual routers and virtual links on top of a physical network. Each virtual node must be located on a physical node, and each virtual link must be routed through a physical path.
  • [13] proposed an integer programming model and solved it using linear relaxation and rounding techniques. With respect to defining the routing and the link capacities in multilevel networks, [18] presented a survey on the models and resolution approaches for the network synthesis problem.
  • the problem is to define the location of data centers and software components, the number of servers in each data center, the information routing, and the capacity of each link in the network.
  • the design objective is to minimize the network delay, the cost, the energy consumption, and the C0 2 emissions. This problem is formalized as an MILP model that minimizes a multi-criteria objective function.
  • the problem setting is an organization that offers cloud services to its users through a data network.
  • the organization must optimize the network design to provide the best possible quality of service while reducing costs, energy consumption, and C0 2 emissions.
  • the applications that will be executed in the network are represented as a graph
  • each node is a software component and each arc is a traffic demand from one software component to another.
  • Each software component may be executed on a user computer or on a server.
  • a single software component can even span multiple servers to achieve its purpose.
  • the applications will be deployed on a computer network
  • G N (V N , A N ) composed of access nodes ⁇ , backbone routers ⁇ , existing data centers ⁇ ), and possible locations to open new data centers L.
  • the set T> denotes all the potential data centers, i.e., the union of £ and T>.
  • Each access node represents an aggregation of users, which may be an Internet Service Provider (ISP), a mobile company, a city, a region, or a country, depending on the case. The users will access the cloud services hosted in the data centers.
  • the nodes in G N are connected through the backbone links, typically by optical fiber.
  • the link capacities, measured in number of channels, are decision variables. Because not every node is suitable to host every software component, P is the set of possible assignments of software components in V A to network nodes in V N .
  • Figure 1 shows an example of a web search engine that must be deployed on a data center network.
  • the application is composed of user interfaces, web servers, and World Wide Web indexes.
  • Each access node aggregates the users of a whole city.
  • There is one user interface for each access node representing the client component that is executed on the user computers of that access node.
  • the same web service may require multiple servers to handle all the requests in an efficient way.
  • the web service makes index queries to the web index where the associations between keywords and pages are stored.
  • Each web index may also require multiple servers with multiple hard disks to store the entire index.
  • W is the master index and all other indexes are replicas of it.
  • the problem is to choose a subset of the potential data centers and locate each web service and each web index in one of the data centers.
  • the following sections provide the parameters and decision variables of the problem.
  • the software components send and receive information between them, and this information is divided into packets that will be sent through network paths.
  • Each step in a path adds processing, waiting, transmission and propagation delay.
  • the link delay is bound by a constant. For example, a specific link could present packet delays of less than 1 millisecond while its traffic is less than 70% of its capacity.
  • the delay incurred by the packets in a path is bound by the sum of the link delay bounds.
  • the strategy in this problem is to locate software components that exchange traffic in close nodes in terms of delay such that the delay is minimized and the quality of service is increased. That implies, for instance, that web service components will be placed near the users to reduce the round trip time.
  • the following is the network traffic notation: b j Throughput of demand d e A A in peak time (in bps).
  • b 2 Capacity per link channel (in bps), e.g., 10 Gbps.
  • Cloud computing applications have software components that are executed either on the user devices or on servers.
  • a single software component may be executed on multiple servers.
  • multiple web servers answer HTTP requests.
  • Each server has capacity allocated for each one of its resources, including network bandwidth, CPU, RAM, and hard disks.
  • the server-related notation is as follows:
  • the servers and switches in a data center are organized in racks, and each data center has space for a maximal number of racks. Each switch can connect a maximal number of servers, which will determine the number of required switches.
  • each software component requires a specific number of servers.
  • the model solution defines what software components each data center hosts, hence the number of servers required in each data center. From that, the number of LAN switches needed for each data center in a particular solution can be calculated. The sum of servers and switches must be less than the data center capacity.
  • An important objective of cloud computing is to minimize the power consumption of the entire network.
  • the power consumption is primarily from the servers, the network switches, the routers, the data center cooling, and the power provisioning equipment.
  • the server power consumption depends on its utilization, having a minimal idle power and reaching a maximal power consumption when the resources are fully utilized [20]. The following is the energy-related notation:
  • Router port power consumption (in watts).
  • PUE Power Usage Effectiveness
  • Average power consumption of a server with a replica of software component i e V A Average power consumption of a server with a replica of software component i e V A .
  • Peak power consumption of a server with a replica of software component i e V A Peak power consumption of a server with a replica of software component i e V A .
  • C0 2 emissions depend on how the energy is produced because each type of energy generates a certain amount of C0 2 per KWh. For example, hydroelectric energy emits 10 g of C0 2 /KWh, and diesel emits 778 g/KWh [21]. Because the power consumption, wi ,
  • Cost of a channel in link e e A N (in $/year).
  • Cost of a server (in $/year).
  • a penalty is applied to the delay for exchanging information between the software components.
  • Some traffic demands may suffer from the delay more than others.
  • a traffic demand could be used for copying files from a data center to calculate statistics once every day. That traffic demand might not be affected by an additional delay; therefore, its delay penalty is low. Traffic demands from the users doing search requests require a very short delay, and in that case, the penalty is high; this penalty represents a loss in revenue because users may change to the competitors.
  • the pollution penalty term comes from two sources.
  • One is the country regulations that apply a carbon tax to the energy consumption.
  • the electricity cost c k is set as the base cost without that tax in data center k
  • c l includes the carbon tax.
  • C0 2 penalty There is a second source of C0 2 penalty that may be even higher: the losses in corporate image.
  • customers reward companies that make efforts to reduce pollution and its quantification should be included in the C0 2 penalty.
  • node k e V N hosts one or more software components; 0 otherwise.
  • each flow variable contains the amount of traffic per demand, per link. These variables are subsequently related to the location variables of the software components because moving a software component moves its traffic demands.
  • Each criterion to optimize is included in a term of the objective function.
  • the following are the formulas to calculate each cost. Note that some of these formulas contain constants to change units, e.g., seconds to milliseconds, KWh to MWh, or Gbps to bps.
  • each demand delay that is the end-to-end delay incurred by packets between software components. For instance, the delay of the demand between a user interface and a web service will be included in this computation. Then, each demand delay is multiplied by its delay penalty, and the total delay penalty is:
  • the network traffic cost is composed of the costs of the LAN switches, the router ports and the links.
  • Each pair of link channels, uplink and downlink, of a link is connected to two router ports, one in each end of the link. Therefore, the number of router ports required for a link is equal to the number of channels.
  • CAPEX capital expenditures
  • the data center operational expenditures includes the human labor, i.e., technicians and security guards, as well as the administrative costs. We exclude the electricity because we consider it in an explicit term.
  • the energy cost for a whole year is calculated as a function of the data center energy consumption, its PUE, and the price of energy as follows:
  • each cost in the objective function is weighted by a parameter, allowing planners to modify the priorities.
  • Equations (2) and (3) state that each software component must be placed in one node, either access node, or data center. They also state that each assignment of a software component to a node must belong to the set of possible assignments P . Constraint (4) specifies that nodes containing software components are open.
  • This constraint relates the software component location to the routing. It associates the application and the network layers. For each demand from software component i e V A to software component j e V A located in nodes k t ,k j ⁇ V N , respectively, the traffic 3 ⁇ 4 enters the network in t and exits in kj . In each intermediate node k from t to ⁇ y , flow is conserved.
  • Constraint (6) defines the link capacity variable b e (in number of channels), requiring that the traffic is less than the maximal utilization threshold, u e b 2 b e . Because full-duplex links are assumed, equation (7) states that the capacity in both directions of the link is the same.
  • the number of servers required in each data center is the sum of the servers required by the software components that are located there. • Number of switches r 6 s k >z k V fee ⁇ (9)
  • each LAN switch can connect a maximum number of servers r 6 t Constraint (9) defines the minimal number of switches, 3 ⁇ 4, required to connect the 3 ⁇ 4 servers in data center k .
  • the number of servers and LAN switches must be less than the maximal capacity of the data center.
  • Equation (11) sums the average amount of power consumed by the server switches, the router ports, and the servers in a node. This value is used to calculate the cost of electricity used in the solution.
  • the peak power consumption of data center k is calculated using the same methodology as for the previous constraint. This value has a fixed limit in each data center because of the electricity available and the power provisioning equipment in the data center.
  • Each variable domain is defined. All the variables are non-negative.
  • the location variables may take the values zero or one.
  • the number of servers, LAN switches, and network channels are integers.
  • the flow and the data center power consumption variables are real numbers.
  • a solution of the proposed tabu search heuristic is a mapping of the software components to the network nodes, i.e., M : V A ⁇ V N .
  • each software component is placed at one of the network nodes.
  • P specifies the set of possible assignments; hence, for each software component i , the pair (i, M(i)) must belong to .
  • the solution space of the proposed heuristic is the set of mappings, as follows:
  • the routing defines the total amount of traffic carried by each link; therefore, we can calculate the minimal number of channels required for that link using the channel capacity, c e t and the maximal allowed utilization ratio per channel, u e , satisfying constraints (6) and (7).
  • Sk are also determined by the software components hosted in data center k through the constraints (8) and (9).
  • Variables w3 ⁇ 4 that correspond to the amount of power consumed by the IT equipment in data center k are calculated with the formula defined in equation (1 1 ).
  • the solutions in S satisfy all the constraints except the data center capacity constraints (10) and (12). Instead of enforcing those constraints, one penalty term for each constraint is added to the objective function.
  • the number of servers that exceeds the capacity in an overloaded data center is multiplied by a large constant, which is higher than all the other terms of the objective function.
  • the same penalty is applied for power consumption that is higher than the data center limit.
  • the tabu search will avoid those solutions because the objective function is much higher than in solutions that do not have overloaded data centers.
  • the algorithm will thus accommodate corner cases, which have capacities that make feasible solutions difficult to find. In that case, the tabu search will search to reduce the overloaded data centers until a feasible solution is found.
  • the greedy heuristic shown in Algorithm 1 locates one software component of V A at a time. Each step chooses the node in V N where the objective function has the lowest value when the software component is placed in that node. Because the objective function includes delay, cost, energy, and C0 2 the node chosen will be the best in those aspects, according to the priorities defined. If the first priority is the delay, a web service will be hosted in the closest data center to its users. If it is the C0 2 , then the data center with the cleanest energy will be chosen. As in every greedy heuristic, the placement is done in an arbitrary order, and that may be non optimal. The greedy algorithm provides a good starting solution that the tabu search improves to a near optimal solution.
  • Figure 2 shows a greedy solution for the example of a web search engine.
  • each one of the user interfaces Ul is located in its correspondent node A, defined in the set P .
  • the objective function remains zero because those components do not require servers and do not have direct traffic demands among them.
  • the location of the web service WSi that serves the requests from U is the node that increments the objective function the least. If all the data centers have the same costs and capacities and all the links have the same delay bound, the best possible data center for WSi is Li , as shown in Figure 2.
  • one software component is moved from one node to another in each iteration of the tabu search.
  • the chosen movement is that with the lowest objective function.
  • the calculation of the objective function for each neighbor can be time consuming for large instances because the number of possible movements is high.
  • the key of the tabu search heuristic is that each iteration must be performed as rapidly as possible. Calculating the objective function in an incremental way achieves that efficiency. Instead of performing the entire calculation for each neighbor, the difference between the current solution and the neighbor solution is calculated. That is, the amount that the objective function would change if a component is moved is what is calculated. In other words, the difference of each objective function term is calculated. Coding this approach with data structures with constant access time is more complicated than the straightforward calculation of the objective function, but the execution time of each iteration is several orders of magnitude shorter when performed incrementally. That means that the tabu search heuristic moves very rapidly among the solutions.
  • Limiting the number of neighbors to analyze can also reduce the execution time of each iteration. Instead of looking at the entire neighborhood, the tabu search analyzes a random subset of them [25].
  • the number of neighbors to analyze is 4,/
  • the multiplier 4 was adjusted through experiments, making a trade-off between the execution time and the solution quality obtained in each iteration.
  • MAX_IT is defined as the sum of the number of nodes in the network and the number of software components, i.e., ⁇ V N ⁇ + ⁇ V A ⁇ .
  • the network nodes V N include a set of access nodes ⁇ , backbone routers ⁇ , and locations for new data centers L.
  • the nodes are distributed among different locations around the globe. To do that, we considered the 500 largest cities in the world [26], the number of Internet users in each of them [27], and their geographic location [28]. For each city, we created an access node in A e V N that aggregates all the traffic of its users. The access nodes are connected through intermediate backbone routers. Finally, for each one of those nodes -routers and access nodes-, a potential data center location was created in L. This method allows us to produce scenarios of different sizes, with up to 500 access nodes and 1000 potential data center locations.
  • a t we used the web search engine topology shown in Figure 1 for n access nodes.
  • the decisions to make are which data centers will host each web service and web index, as well as all the network planning variables of the proposed model, to minimize the delay, the cost, energy consumption, and the C0 2 emissions.
  • the total inter-domain traffic of the Internet was 154 Tbps in March 2013, considering the estimation of 39 Tbps in July 2009 [29] and a 45.5% annual growth.
  • the hypothetical web search engine analyzed manages 1 % of the Internet traffic, that is 1 .54 Tbps. That total amount of traffic was distributed among the access nodes in proportion to the number of Internet users in each city [27]. Those are the traffic demands between the user interfaces
  • the average packet size is 770 bytes ( ), the capacity of each link channel is 10 Gbps (b 2 ), the maximal channel utilization is 70% and the maximal link delay is 1 ms plus the propagation delay (t e ).
  • the propagation delay of each link is calculated with the distance between the nodes divided by the speed of light over optical fiber cable (200,000 km/s).
  • the number of servers required is 200,000, also distributed in proportion to the number of users in each city. From the servers required by each city, 60% are for web
  • Each data center can accommodate either 40, 80, 160, 1 ,000, 32,000, 64,000, or 128,000 IT elements.
  • Each LAN switch can connect up to 40 r 6
  • the average power consumption of each server ( w i ) was defined as 150 W, and the
  • the data center power capacity for IT equipment ( w l ) is either 12 KW, 24 KW, 49 KW, 303 KW, 9.7 MW, 19.4 MW, or 38.7 MW, depending on the data center size.
  • the PUE of the data centers is 1 .08 ( w l ), that is the power provisioning equipment, cooling artifacts and any other power consumption are 8% of the power consumed by the IT equipment.
  • the type of energy that supplies a data center generates a specific amount of C0 2 emissions ( w t ): offshore wind produces 9 g / KWh, hydroelectric 10 g / KWh, geothermal 38 g / KWh, nuclear 66 g / KWh, natural gas 443 g / KWh, diesel 778 g / KWh, and coal 960 g / KWh [21 ].
  • the amount of C0 2 produced by each data center in this case was randomly chosen from one of those values.
  • the delay penalty was defined as $10,000 / (ms / packet) / year for the traffic demands between the Ullustrated WS complaint and Wlpit and $10 / (ms / packet) / year for the delay between the Wl, themselves ( c d ). That means that the delay between Ullustrated WS bulk and Wl, is much more important than the delay between the Wl, themselves.
  • the cost of each LAN switch is $1 ,000
  • Each 1 0 Gbps router port costs $100 / year (c 3 ) and each directed 10 Gbps link channel, $60,000 / year (c 4 e ) [30].
  • the cost of each server is $4,000 [31 ] and it is amortized in 4 years, resulting in $1 ,000 / year (c 5 ).
  • the data center CAPEX is $12 / W amortized in
  • the network has 10 access nodes and 10 potential data center locations.
  • the access nodes are shown as black circles and the potential data center locations are shown as white boxes.
  • the application graph is a web search engine with a user interface, a web service, and a web index for each access node, and the topology of traffic demands as discussed in Section 5.
  • Solution A 10 data centers are open, i.e., all of them are used to host servers and software components. That optimization initially favors the delay because the delay penalty is the highest term in the objective function at $ 856.2 M.
  • all the access nodes have a close data center that serves their requests, and the average delay is 4.4 ms.
  • the total power consumed by all the data centers is 34.6 MW.
  • the data centers are powered with all types of energy, in particular, 4 of them have high pollution emission ratios, between 443 and 960 grams of C0 2 per KWh. The total C0 2 emission is 74,695 ton per year, and the total cost is $ 678.1 M per year.
  • Solution B is obtained by augmenting the pollution multiplier in the objective function (ip) to 1000. That makes the C0 2 penalty term greater than the delay penalty term.
  • the optimal solution is to open 4 data centers. Three of the data centers are powered with hydro-electric energy producing 10 grams of C0 2 per KWh, and one of the centers is powered with geothermal energy introducing 38 grams of C0 2 per KWh. The total C0 2 emissions are 3,561 ton per year, 21 -times smaller than Solution A. The C0 2 penalty decreases with the same ratio. The energy consumption remains the same because most of it depends on the number of servers used and the of the data centers, and in these cases, both remain constant. The total cost is 68% of Solution A because fewer data centers are used; therefore, the CAPEX and the OPEX are reduced. The average delay is 12.2 ms, which is 2.8-times greater than Solution A.
  • Solution C is obtained by setting the cost multipliers to 1000, making the total cost the first priority to optimize.
  • the number of open data centers is 3, and they have the minimal capacity to accommodate all the servers.
  • the total cost is reduced to $ 390.5 M, the total C0 2 emissions are 41 ,181 tons per year, and the average delay is 21.2 ms.
  • Solution B appears to be a good compromise between the network performance, pollution, and cost because the four chosen data centers imply a short delay with an acceptable cost and very low C0 2 emissions.
  • planners can evaluate multiple alternative solutions and analyze the tradeoffs between them.
  • additional constraints can be added to evaluate solutions around the optimal values. The execution time to find these solutions was between 0.7 s and 178 s. We will see how the Cplex execution time increases very quickly as the network growths and how the tabu search performs very well for small and large cases.
  • the minimal gaps are between 0 and 1.95%; most of them are below 0.1 %, showing that the heuristic is very effective in finding near- optimal solutions.
  • the last column of the tables exhibits the gap between the value found with the greedy heuristic and the optimal value. In two cases, greedy found the optimal values but most of the cases were above 10%. That reveals that the tabu search is what effectively improves the solution to near optimality.
  • the execution time of the tabu search ranged from less than 1 millisecond to 868 milliseconds, whereas Cplex used more than 58 minutes for the most difficult case.
  • the tabu search heuristic had an average gap of 1.95% and an average execution time of 868 milliseconds.
  • the results also show that the cost-oriented optimization requires more execution time in all the cases. This extra execution time is needed because there are many near-optimal solutions that cannot be discarded until the optimum is found. In the delay-oriented case, the feasible solutions with small delay are those with servers near the users.
  • the tabu search heuristic is very efficient for small and medium problems. We will analyze larger cases, i.e., 50 to 500 access nodes and 100 to 1000 potential data center locations, to gauge the performance of the tabu search heuristic for large problems. Considering a large number of cities makes it possible to guarantee a good quality of service for most of the Internet users around the world. Large networks, such as Akamai, have small data centers in more than a thousand locations around the world to guarantee a high quality of service [5], which is why it is important to analyze the large cases using an efficient heuristic.
  • the data center sizes were 40, 80, 160, 1 ,000, 32,000, 64,000, or 128,000 IT elements; solutions may contain multiple small data centers or a combination of small, medium and large data centers.
  • Tables 6, 7 and 8 show the network delay, costs, optimal values, and execution times for these cases. Each reported value is the average of 10 executions.
  • the results show that the execution time was less than 1 minute for the cases with up to 200 access nodes and 400 potential data centers, and it was less than 10 minutes in every instance.
  • the delay was held to approximately 5 ms in the delay-oriented optimization by adding more data centers.
  • the smallest case opened 34 data centers, and the largest case opened 396, i.e., 12-times more, but the cost was only multiplied by 4.5 because smaller data centers can be used.
  • the C0 2 emissions were high compared to the pollution minimization cases.
  • the average C0 2 in the first table is 99,557 tons, and it is 7,295 tons in the second table.
  • the average delay in the C0 2 optimization only increased to 5.8 ms, which is less than 1 ms more than the delay-oriented optimization.
  • the average cost decreased from $ 3,543.0 M to $ 3,000.5 M, which are both very large compared to the average optimal cost of $ 531.8 M in the third case.
  • the average cost was 18% of the cost of the C0 2 strategy, though the average delay increased to 22 ms, more than four-times that in the delay-oriented strategy.
  • Cloud computing is expanding to increasingly more users, applications and devices; therefore, the network needs to grow in an efficient and sustainable way.
  • the location of data centers, servers, and software components impacts the network efficiency, the pollution, and the total cost.
  • the definition of the network link capacities and the information routing is also very important.
  • the proposed model allows planners to evaluate different solutions and to make variations in the optimization priorities.
  • the AMPL-Cplex implementation is useful for analyzing small cases, but its algorithmic complexity makes the execution time too high for real instances.
  • the deployment of global networks needs the evaluation of the traffic from many cities around the world, and hundreds of potential data center locations.
  • the tabu search algorithm evaluates potential neighbor solutions by calculating the difference from the current solution using the right data structures. The results showed that the tabu search heuristic could find near optimal solutions in a very short execution time. Instances of up to 500 access nodes and 1000 potential data center locations were solved in less than 10 minutes, showing that every real instance can be modeled and solved in this fashion.
  • the mathematical model and the algorithm can be extended in multiple directions.
  • One direction is the server virtualization, a key aspect of cloud computing. With that feature, each server can host multiple independent virtual machines. Then, fewer servers are used, reducing costs and energy consumption.
  • Another possible extension is to consider dynamic demands. In that case, the variations of the resource requirements in each hour of the day and on each day of the week are considered; therefore, the solutions make better use of the data centers, servers and link capacities.
  • FIG. 8 there is shown a method 10 for locating data centers or points of presence and software components in a cloud computing network, according to a preferred embodiment of the present invention.
  • the method begins by modeling 12 a network using a mixed integer linear programming (MILP) model and an objective function.
  • the next step is minimizing 14 the objective function by means of an optimization solver and a tabu search heuristic.
  • next steps is simultaneously determining 16 an optimal location of data centers or points of presence and data software components in the network and simultaneously determining 18 optimal capacities for backbone links that connect data centers or points of presence, the routing and the number of servers in each data centers or point of presence of the network.
  • MILP mixed integer linear programming
  • a system for locating data centers or points of presence and software components in a cloud computing network comprising: a mixed integer linear programming (MILP) model for execution in a computer including an objective function for modeling said network; an optimization solver for minimizing said objective function using a tabu search heuristic, wherein the solver is configured to: simultaneously determine an optimal location of said data centers or points of presence and said data software components in said network; simultaneously determine optimal capacities for the backbone links that connect said data centers or points of presence, the routing and the number of servers in each said data centers or point of presence of said network.
  • MILP mixed integer linear programming
  • a system for locating data centers or points of presence and software components in a cloud computing network comprising: a memory configured to store program instructions and data; a processor configured to read the program instructions from the memory, wherein, in response to execution of the program instructions, the processor is operable to: model said network using a mixed integer linear programming (MILP) model and an objective function; minimize said objective function by means of an optimization solver and a tabu search heuristic; simultaneously determine an optimal location of said data centers or points of presence and said data software components in said network; simultaneously determine optimal capacities for backbone links that connect said data centers or points of presence, the routing and the number of servers in each said data centers or point of presence of said network.
  • MILP mixed integer linear programming
  • a computer-readable memory medium configured to store program instructions for locating data centers or points of presence and software components in a cloud computing network, wherein the program instructions are configured to direct one or more computers to perform operations comprising: modeling said network using a mixed integer linear programming (MILP) model and an objective function; minimizing said objective function by means of an optimization solver and a tabu search heuristic; simultaneously determining an optimal location of said data centers or points of presence and said data software components in said network; simultaneously determining optimal capacities for backbone links that connect said data centers or points of presence, the routing and the number of servers in each said data centers or point of presence of said network.
  • MILP mixed integer linear programming
  • Rabkin, I. Stoica, and M. supplementia "Above the clouds: A Berkeley view on cloud computing," University of California at Berkeley, Tech. Rep. UCB/EECS-2009-28, 2009.

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  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
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Abstract

L'ubiquité des applications dans le nuage nécessite une conception méticuleuse des réseaux en nuage ayant une haute qualité de service, des coûts bas et des émissions en CO2 basses. La présente invention concerne un procédé et un système permettant l'optimisation des localisations de centres de données en nuage ou de points d'occupation ainsi que des composants logiciels tout en trouvant simultanément les capacités de routage d'information et de liaison réseau en utilisant une euristique de recherche tabou extrêmement efficace. Les objectifs sont d'optimiser la performance du réseau, les émissions en CO2, les dépenses d'investissement de capital (CAPEX), et les dépenses d'exploitation (OPEX). Le problème est modélisé en utilisant un modèle de programmation partiellement en nombres entiers, et il est résolu avec à la fois un solveur d'optimisation et une euristique de recherche tabou.
PCT/CA2014/050623 2013-06-28 2014-06-30 Procédé et système d'optimisation de localisation de centres de données ou de points d'occupation et composants logiciels dans un réseau informatique en nuage utilisant un algorithme de recherche tabou WO2014205585A1 (fr)

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CN109492800A (zh) * 2017-11-24 2019-03-19 华东理工大学 一种用于自动化仓库的车辆路径优化方法
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