We extend the Mobile Server Problem, introduced in SPAA'17, to a model where k identical mobile resources, here named servers, answer requests appearing at points in the Euclidean space. In order to reduce communication costs, the positions of the servers can be adapted by a limited distance m_s per round for each server. The costs are measured similar to the classical Page Migration Problem, i.e., answering a request induces costs proportional to the distance to the nearest server, and moving a server induces costs proportional to the distance multiplied with a weight D. We show that, in our model, no online algorithm can have a constant competitive ratio, i.e., one which is independent of the input length n, even if an augmented moving distance of (1+ )m_s is allowed for the online algorithm. Therefore we investigate a restriction of the power of the adversary dictating the sequence of requests: We demand locality of requests, i.e., that consecutive requests come from points in the Euclidean space with distance bounded by some constant m_c. We show constant lower bounds on the competitiveness in this setting (independent of n, but dependent on k, m_s and m_c). On the positive side, we present a deterministic online algorithm with bounded competitiveness when augmented moving distance and locality of requests is assumed. Our algorithm simulates any given algorithm for the classical k-Page Migration problem as guidance for its servers and extends it by a greedy move of one server in every round. The resulting competitive ratio is polynomial in the number of servers k, the ratio between m_c and m_s, the inverse of the augmentation factor 1 and the competitive ratio of the simulated k-Page Migration algorithm.
This paper deals with the work function algorithm (WFA) for solving the on-line k-server problem. The paper addresses some practical aspects of the WFA, such as its efficient implementation and its true quality of serving. First, an implementation of the WFA is proposed, which is based on network flows, and which reduces each step of the WFA to only one minimal-cost maximal flow problem instance. Next, it is explained how the proposed implementation can further be simplified if the involved metric space is finite. Also, it is described how actual computing of optimal flows can be speeded up by taking into account special properties of the involved networks. Some experiments based on the proposed implementation and improvements are presented, where actual serving costs of the WFA have been measured on very large problem instances and compared with costs of other algorithms. Finally, suitability of the WFA for solving real-life problems is discussed. We prove that the work function algorithm for the k -server problem has a competitive ratio at most 2 k −1. [1988] conjectured that the competitive ratio for the k -server problem is exactly k (it is trivially at least k ); previously the best-known upper bound was exponential in k . Our proof involves three crucial ingredients: A quasiconvexity property of work functions, a duality lemma that uses quasiconvexity to characterize the configuration that achieve maximum increase of the work function, and a potential function that exploits the duality lemma.  In the k-server problem, one must choose how k mobile servers will serve each of a sequence of requests, making decisions in an online manner. An optimal deterministic online strategy is exhibited when the requests fall on the real line. For the weighted-cache problem, in which the cost of moving to x from any other point is @math , the weight of x, an optimal deterministic algorithm is also provided. The nonexistence of competitive algorithms for the asymmetric two-server problem and of memoryless algorithms for the weighted-cache problem is proved. A fast algorithm for oflline computing of an optimal schedule is given, and it is shown that finding an optimal offline schedule is at least as hard as the assignment problem. The k-server problem is one of the most fundamental online problems. The problem is to schedule k mobile servers to visit a sequence of points in a metric space with minimum total mileage. The k-server conjecture of Manasse, McGeogh, and Sleator states that there exists a k-competitive online algorithm. The conjecture has been open for over 15 years. The top candidate online algorithm for settling this conjecture is the work function algorithm (WFA) which was shown to have competitive ratio at most 2k - 1. In this paper, we lend support to the conjecture that WFA is in fact k-competitive by proving that it achieves this ratio in several special metric spaces: the line, the star, and all metric spaces with k + 2 points.