Exploring heterogeneity of unreliable machines for p2p backup

P2P architecture is a viable option for enterprise backup. In contrast to dedicated backup servers, nowadays a standard solution, making backups directly on organization's workstations should be cheaper (as existing hardware is used), more efficient (as there is no single bottleneck server) and more reliable (as the machines are geographically dispersed). We present the architecture of a p2p backup system that uses pairwise replication contracts between a data owner and a replicator. In contrast to standard p2p storage systems using directly a DHT, the contracts allow our system to optimize replicas' placement depending on a specific optimization strategy, and so to take advantage of the heterogeneity of the machines and the network. Such optimization is particularly appealing in the context of backup: replicas can be geographically dispersed, the load sent over the network can be minimized, or the optimization goal can be to minimize the backup/restore time. However, managing the contracts, keeping them consistent and adjusting them in response to dynamically changing environment is challenging. We built a scientific prototype and ran the experiments on 150 workstations in the university's computer laboratories and, separately, on 50 PlanetLab nodes. We found out that the main factor affecting the quality of the system is the availability of the machines. Yet, our main conclusion is that it is possible to build an efficient and reliable backup system on highly unreliable machines (our computers had just 13% average availability)

Cooperation and Competition when Bidding for Complex Projects: Centralized and Decentralized Perspectives

To successfully complete a complex project, be it a construction of an airport or of a backbone IT system, agents (companies or individuals) must form a team having required competences and resources. A team can be formed either by the project issuer based on individual agents' offers (centralized formation); or by the agents themselves (decentralized formation) bidding for a project as a consortium---in that case many feasible teams compete for the contract. We investigate rational strategies of the agents (what salary should they ask? with whom should they team up?). We propose concepts to characterize the stability of the winning teams and study their computational complexity

Fair non-monetary scheduling in federated clouds

In a hybrid cloud, individual cloud service providers (CSPs) often have incentive to use each other's resources to off-load peak loads or place load closer to the end user. However, CSPs have to keep track of contributions and gains in order to disincentivize long-term free-riding. We show CloudShare, a distributed version of a load balancing algorithm DirectCloud based on the Shapley value---a powerful fairness concept from game theory. CloudShare coordinates CSPs by a ZooKeeper-based coordination layer; each CSP runs a broker that interacts with local resources (such as Kubernetes-managed clusters). We quantitatively evaluate our implementation by simulation. The results confirm that CloudShare generates on the average more fair schedules than the popular FairShare algorithm. We believe our results show an viable alternative to monetary methods based on, e.g., spot markets.Comment: Accepted to CrossCloud'18: 5th Workshop on CrossCloud Infrastructures & Platform

Optimizing egalitarian performance when colocating tasks with types for cloud data center resource management

International audienceIn data centers, up to dozens of tasks are colocated on a single physical machine. Machines are used more efficiently, but the performance of the tasks deteriorates, as the colocated tasks compete for shared resources. Since the tasks are heterogeneous, the resulting performance dependencies are complex. In our previous work [26], [27] we proposed a new combinatorial optimization model that uses two parameters of a task-its size and its type-to characterize how a task influences the performance of other tasks allocated to the same machine. In this paper, we study the egalitarian optimization goal: the aim is to optimize the performance of the worst-off task. This problem generalizes the classic makespan minimization on multiple processors (P||C max). We prove that polynomially-solvable variants of P||C max are NP-hard for this generalization, and that the problem is hard to approximate when the number of types is not constant. For a constant number of types, we propose a PTAS, a fast approximation algorithm, and a series of heuristics. We simulate the algorithms on instances derived from a trace of one of Google clusters. Compared with baseline algorithms solving P||C max , our proposed algorithms aware of the types of the jobs lead to significantly better tasks' performance. The notion of type enables us to extend standard combinatorial optimization methods to handle degradation of performance caused by colocation. Types add a layer of additional complexity. However, our results-approximation algorithms and good average-case performance-show that types can be handled efficiently

Collective Schedules: Scheduling Meets Computational Social Choice

International audienceWhen scheduling public works or events in a shared facility one needs to accommodate preferences of a population. We formalize this problem by introducing the notion of a collective schedule. We show how to extend fundamental tools from social choice theory—positional scoring rules, the Kemeny rule and the Con-dorcet principle—to collective scheduling. We study the computational complexity of finding collective schedules. We also experimentally demonstrate that optimal collective schedules can be found for instances with realistic sizes