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Identifying and using energy-critical paths
- In CoNEXT
, 2011
"... The power consumption of the Internet and datacenter networks is already significant, and threatens to shortly hit the power delivery limits while the hardware is trying to sustain ever-increasing traffic requirements. Existing energyreduction approaches in this domain advocate recomputing network c ..."
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The power consumption of the Internet and datacenter networks is already significant, and threatens to shortly hit the power delivery limits while the hardware is trying to sustain ever-increasing traffic requirements. Existing energyreduction approaches in this domain advocate recomputing network configuration with each substantial change in demand. Unfortunately,computingtheminimumnetworksubset is computationally hard and does not scale. Thus, the network is forced to operate with diminished performance during the recomputation periods. In this paper, we propose REsPoNse, a framework which overcomes the optimalityscalability trade-off. The insight in REsPoNse is to identify afewenergy-criticalpathsoff-line,installthemintonetwork elements, and use a simple online element to redirect the trafficinawaythatenableslargepartsofthenetwork toenter a low-power state. We evaluate REsPoNse with real network data and demonstrate that it achieves the same energy savingsasthe existingapproaches,with marginal impact on network scalabilityandapplicationperformance. 1.
Multicast Routing for Energy Minimization Using Speed Scaling
"... Abstract. We consider virtual circuit multicast routing in a network of links that are speed scalable. We assume that a link with load f uses power σ + f α,whereσ is the static power, and α>1 is some constant. We assume that a link may be shutdown if not in use. In response to the arrival of clie ..."
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Abstract. We consider virtual circuit multicast routing in a network of links that are speed scalable. We assume that a link with load f uses power σ + f α,whereσ is the static power, and α>1 is some constant. We assume that a link may be shutdown if not in use. In response to the arrival of client i at vertex ti a routing path (the virtual circuit) Pi connecting a fixed source s to sink ti must be established. The objective is to minimize the aggregate power used by all links. We give a polylog-competitive online algorithm, and a polynomialtime O(α)-approximation offline algorithm if the power functions of all links are the same. If each link can have a different power function, we show that the problem is APX-hard. If additionally, the edges may be directed, then we show that no poly-log approximation is possible in polynomial time under standard complexity assumptions. These are the first results on multicast routing in speed scalable networks in the algorithmic literature. 1
Greendcn: A general framework for achieving energy efficiency in data center networks
- IEEE Journal on Selected Areas in Communications
, 2014
"... ar ..."
Hallucination Helps: Energy Efficient Virtual Circuit Routing
, 2013
"... We consider virtual circuit routing protocols, with an objective of minimizing energy, in a network of components that are speed scalable, and that may be shutdown when idle. We assume that the speed s of the router is proportional to its load, and assume the standard model for component power, name ..."
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We consider virtual circuit routing protocols, with an objective of minimizing energy, in a network of components that are speed scalable, and that may be shutdown when idle. We assume that the speed s of the router is proportional to its load, and assume the standard model for component power, namely that the power is some constant static power plus sα, where typically α ∈ [1.1, 3]. We give a polynomial-time offline algorithm that is the combination of three natural combinatorial algorithms, and show that for any fixed α the algorithm has approximation ratio O(logα k), where k is the number of demand pairs. The algorithm extends rather naturally to a randomized online algorithm, which we show has competitive ratio Õ(log3α+1 k). This is the first online result for the problem. We also show that this online algorithm has competitive ratio Õ(logα+1 k) for the case that all connections have a common source. 1
Limits of Energy Saving for the Allocation of Data Center Resources to Networked Applications
"... Abstract-Energy related costs are becoming one of the largest contributors to the overall cost of operating a data center, whereas the degree of data center utilization continues to be very low. Energy-aware dynamic provision of resources based on the consolidation of existing application instances ..."
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Abstract-Energy related costs are becoming one of the largest contributors to the overall cost of operating a data center, whereas the degree of data center utilization continues to be very low. Energy-aware dynamic provision of resources based on the consolidation of existing application instances can simultaneously address under-utilization of servers while highly reducing energy costs. Thus, energy costs cannot be treated separately from resource provision and allocation. However, current scheduling techniques based on market mechanisms do not specifically deal with such scenario. In this paper we model the problem of minimizing energy consumption of the allocation of resources to networked applications as a Stackelberg leadership game to find an upper bound of energy saving. The model is applied to a proportional-share mechanism where resource providers can maximize their profit by minimizing energy costs while users can select resources ensuring the minimum requirements are satisfied. We show that our mechanism can determine the optimal set of resources on and off, even in realistic conditions considering incomplete information, and heterogeneous applications. I. INTRODUCTION Energy consumption is a key concern in networked computing systems, including service overlays, content distribution systems and many other distributed systems. These systems require a collection of networked computing resources from one or multiple providers on data centers spread over the world. All data centers or cloud computing providers face the problems of high energy costs, a discouraging low server utilization, and changing resource demand of applications. For instance, Hoelzle et al. Two popular methods for effectively matching power consumption to workload requirements in a data center are via processor speed scaling and powering down nodes. The goal of this paper is to develop a theoretical framework to analyze the limits of energy saving, and strategies to determine the right set of computing nodes on and off to minimize energy consumption while keeping the right level of service for networked applications using these computational resources. We consider several characteristics of modern applications and resources -e.g. proportional share of resources, granularity of application processes, level of parallelism, and migration and consolidation of virtual machines. The focus and contributions of this paper are as follows: i) we derive a simple energetic model for computing elements based on SPEC benchmarks using real data; ii) we model
2.2 Energy Model............................. 5
"... I hereby certify that this material, which I now submit for assessment on the programme of study leading to the award of Master of Science is entirely my own work and has not been taken from the work of others save and to the extent that such work has been cited and acknowledged within the text of m ..."
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I hereby certify that this material, which I now submit for assessment on the programme of study leading to the award of Master of Science is entirely my own work and has not been taken from the work of others save and to the extent that such work has been cited and acknowledged within the text of my work.
Load Balance vs Energy Efficiency in Traffic Engineering: A Game Theoretical Perspective
"... Abstract—In this paper, we study the tradeoff between two important traffic engineering objectives: load balance and energy efficiency. Although traditional multi-objective optimization methods can yield a Pareto efficient solution, they need to construct an aggregate objective function (AOF) or mod ..."
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Abstract—In this paper, we study the tradeoff between two important traffic engineering objectives: load balance and energy efficiency. Although traditional multi-objective optimization methods can yield a Pareto efficient solution, they need to construct an aggregate objective function (AOF) or model one of the two objectives as a constraint in the optimization problem formulation. As a result, it is difficult to achieve a fair tradeoff between the both objectives. Accordingly, we induce a Nash bargaining framework which treats the two objectives as two virtual players in a game theoretic model, who negotiate how traffic should be routed in order to optimize both objectives. During the negotiation, each of them announces its performance threat value to reduce its cost, so the model is regarded as a threat value game. Our analysis shows that no agreement can be achieved if each player sets its threat value to be its best performance. To avoid such a negotiation break-down, we modify the threat value game to have a repeated process and design a mechanism to not only guarantee an agreement, but also generate a fair solution. In addition, the insights from this work are also useful for achieving a fair tradeoff in other multi-objective optimization problems.
Power-proportional Router: Architectural Design and Experimental Evaluation
"... Abstract—High speed routers in Internet are becoming in-creasingly more powerful, as well as more energy hungry. However, they always show power-inefficient property due to we unilaterally in pursuit of high speed before. In response to this problem, we present a power-efficient router architecture ..."
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Abstract—High speed routers in Internet are becoming in-creasingly more powerful, as well as more energy hungry. However, they always show power-inefficient property due to we unilaterally in pursuit of high speed before. In response to this problem, we present a power-efficient router architecture named GreenRouter in this paper. GreenRouter separates a line card into two parts physically: the network interface card (named as DB) and the packet processing card (named as MB), which are interconnected by a two-stage unidirectional switch fabric. Traffic from all the DBs shares all the MBs in GreenRouter, thus the traffic can be aggregated to a few active MBs when traffic is light and the inactive MBs can be shut down to save power. We give the detailed architectural design of GreenRouter. Real-trace driven experiments show that GreenRouter can save about 50% power compared to the conventional router when the average traffic load is 30%, while providing quality of service guarantee at the same time. I.
Greening the Internet: Energy-Optimal File Distribution
"... Abstract—Despite file distribution applications are responsi-ble for a major portion of the current Internet traffic, so far little effort has been dedicated to study file distribution from the point of view of energy efficiency. In this paper, we present the first extensive and detailed theoretical ..."
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Abstract—Despite file distribution applications are responsi-ble for a major portion of the current Internet traffic, so far little effort has been dedicated to study file distribution from the point of view of energy efficiency. In this paper, we present the first extensive and detailed theoretical study for the problem of energy efficiency in file distribution. Specifically, we first demonstrate that the general problem of minimizing energy consumption in file distribution is NP-hard. For restricted versions of the problem, we derive tight lower bounds on energy consumption, and we design a family of algorithms that achieve these bounds. Our results prove that through collaborative p2p schemes up to 50 % energy savings are achievable with respect to the best available centralized file distribution scheme. Through simulation, we show that even in heterogeneous settings (e.g., considering network congestion, and link variability across hosts) our collaborative algorithms always achieve significant energy savings with respect to the power consumption of centralized file distribution systems. I.
unknown title
"... Abstract—Despite file-distribution applications are responsible for a major portion of the current Internet traffic, so far little effort has been dedicated to study file distribution from the point of view of energy efficiency. In this paper, we present a first approach at the problem of energy eff ..."
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Abstract—Despite file-distribution applications are responsible for a major portion of the current Internet traffic, so far little effort has been dedicated to study file distribution from the point of view of energy efficiency. In this paper, we present a first approach at the problem of energy efficiency for file distribution. Specifically, we first demonstrate that the general problem of minimizing energy consumption in file distribution in heterogeneous settings is NP-hard. For homogeneous settings, we derive tight lower bounds on energy consumption, and we design a family of algorithms that achieve these bounds. Our results prove that collaborative p2p schemes achieve up to 50% energy savings with respect to the best available centralized file distribution scheme. Through simulation, we demonstrate that in more realistic cases (e.g., considering network congestion, and link variability across hosts) we validate this observation, since our collaborative algorithms always achieve significant energy savings with respect to the power consumption of centralized file distribution systems. I.
