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Abstract It is natural to generalize the online$$k$$ $k$-Server problem by allowing each request to specify not only a pointp, but also a subsetSof servers that may serve it. To date, only a few special cases of this problem have been studied. The objective of the work presented in this paper has been to more systematically explore this generalization in the case of uniform and star metrics. For uniform metrics, the problem is equivalent to a generalization of Paging in which each request specifies not only a pagep, but also a subsetSof cache slots, and is satisfied by having a copy ofpin some slot inS. We call this problemSlot-Heterogenous Paging. In realistic settings only certain subsets of cache slots or servers would appear in requests. Therefore we parameterize the problem by specifying a family$${\mathcal {S}}\subseteq 2^{[k]}$$ $S\subseteq 2^{\left[k\right]}$of requestable slot sets, and we establish bounds on the competitive ratio as a function of the cache sizekand family$${\mathcal {S}}$$ $S$:If all request sets are allowed ($${\mathcal {S}}=2^{[k]}\setminus \{\emptyset \}$$ $S=2^{\left[k\right]}\backslash \left\{\varnothing \right\}$), the optimal deterministic and randomized competitive ratios are exponentially worse than for standard Paging ($${\mathcal {S}}=\{[k]\}$$ $S=\left\{\right[k\left]\right\}$).As a function of$$|{\mathcal {S}}|$$ $\left|S\right|$andk, the optimal deterministic ratio is polynomial: at most$$O(k^2|{\mathcal {S}}|)$$ $O\left(k^2\right|S\left|\right)$and at least$$\Omega (\sqrt{|{\mathcal {S}}|})$$ $\Omega \left(\sqrt{\left|S\right|}\right)$.For any laminar family$${\mathcal {S}}$$ $S$of heighth, the optimal ratios areO(hk) (deterministic) and$$O(h^2\log k)$$ $O(h^{2}logk)$(randomized).The special case of laminar$${\mathcal {S}}$$ $S$that we callAll-or-One Pagingextends standard Paging by allowing each request to specify a specific slot to put the requested page in. The optimal deterministic ratio forweightedAll-or-One Paging is$$\Theta (k)$$ $\Theta \left(k\right)$. Offline All-or-One Paging is$$\mathbb{N}\mathbb{P}$$ $NP$-hard.Some results for the laminar case are shown via a reduction to the generalization of Paging in which each request specifies a set$$P$$ $P$ofpages, and is satisfied by fetching any page from$$P$$ $P$into the cache. The optimal ratios for the latter problem (with laminar family of heighth) are at mosthk(deterministic) and$$hH_k$$ $h{H}_k$(randomized). 

In this paper, we study the weighted k-server problem on the uniform metric in both the offline and online settings. We start with the offline setting. In contrast to the (unweighted) k-server problem which has a polynomial-time solution using min-cost flows, there are strong computational lower bounds for the weighted k-server problem, even on the uniform metric. Specifically, we show that assuming the unique games conjecture, there are no polynomial-time algorithms with a sub-polynomial approximation factor, even if we use c-resource augmentation for c < 2. Furthermore, if we consider the natural LP relaxation of the problem, then obtaining a bounded integrality gap requires us to use at least 𝓁 resource augmentation, where 𝓁 is the number of distinct server weights. We complement these results by obtaining a constant-approximation algorithm via LP rounding, with a resource augmentation of (2+ε)𝓁 for any constant ε > 0. In the online setting, an exp(k) lower bound is known for the competitive ratio of any randomized algorithm for the weighted k-server problem on the uniform metric. In contrast, we show that 2𝓁-resource augmentation can bring the competitive ratio down by an exponential factor to only O(𝓁² log 𝓁). Our online algorithm uses the two-stage approach of first obtaining a fractional solution using the online primal-dual framework, and then rounding it online. 

Online allocation is a broad class of problems where items arriving online have to be allocated to agents who have a fixed utility/cost for each assigned item so to maximize/minimize some objective. This framework captures a broad range of fundamental problems such as the Santa Claus problem (maximizing minimum utility), Nash welfare maximization (maximizing geometric mean of utilities), makespan minimization (minimizing maximum cost), minimization of 𝓁_p-norms, and so on. We focus on divisible items (i.e., fractional allocations) in this paper. Even for divisible items, these problems are characterized by strong super-constant lower bounds in the classical worst-case online model. In this paper, we study online allocations in the learning-augmented setting, i.e., where the algorithm has access to some additional (machine-learned) information about the problem instance. We introduce a general algorithmic framework for learning-augmented online allocation that produces nearly optimal solutions for this broad range of maximization and minimization objectives using only a single learned parameter for every agent. As corollaries of our general framework, we improve prior results of Lattanzi et al. (SODA 2020) and Li and Xian (ICML 2021) for learning-augmented makespan minimization, and obtain the first learning-augmented nearly-optimal algorithms for the other objectives such as Santa Claus, Nash welfare, 𝓁_p-minimization, etc. We also give tight bounds on the resilience of our algorithms to errors in the learned parameters, and study the learnability of these parameters. 

In this article, we initiate the study of the weighted paging problem with predictions. This continues the recent line of work in online algorithms with predictions, particularly that of Lykouris and Vassilvitski (ICML 2018) and Rohatgi (SODA 2020) on unweighted paging with predictions. We show that unlike unweighted paging, neither a fixed lookahead nor a knowledge of the next request for every page is sufficient information for an algorithm to overcome the existing lower bounds in weighted paging. However, a combination of the two, which we call strong per request prediction (SPRP), suffices to give a 2-competitive algorithm. We also explore the question of gracefully degrading algorithms with increasing prediction error, and give both upper and lower bounds for a set of natural measures of prediction error.