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Unexpected long query latency of a database system can cause domino effects on all the upstream services and se- verely degrade end users’ experience with unpredicted long waits, resulting in an increasing number of users disengaged with the services and thus leading to a high user disengage- ment ratio (UDR). A high UDR usually translates to reduced revenue for service providers. This paper proposes UTSLO, a UDR-oriented SLO guaranteed system, which enables a database system to support multi-tenant UDR targets in a cost-effective fashion through UDR-oriented capacity plan- ning and dynamic UDR target enforcement. The former aims to estimate the feasibility of UDR targets while the latter dynamically tracks and regulates per-connection query la- tency distribution needed for accurate UDR target guarantee. In UTSLO, the database service capacity can be fully ex- ploited to efficiently accommodate tenants while minimizing resources required for UDR target guarantee. 

A primary design objective for Data-intensive User- facing (DU) services for cloud and edge computing is to maximize query throughput, while meeting query tail latency Service Level Objectives (SLOs) for individual queries. Unfortunately, the existing solutions fall short of achieving this design objective, which we argue, is largely attributed to the fact that they fail to take the query fanout explicitly into account. In this paper, we propose TailGuard based on a Tail-latency-SLO-and- Fanout-aware Earliest-Deadline-First Queuing policy (TF-EDFQ) for task queuing at individual task servers the query tasks are fanned out to. With the task queuing deadline for each task being derived based on both query tail latency SLO and query fanout, TailGuard takes an important first step towards achieving the design objective. TailGuard is evaluated against First-In-First-Out (FIFO) task queuing, task PRIority Queuing (PRIQ) and Tail-latency-SLO-aware EDFQ (T-EDFQ) policies by simulation. It is driven by three types of applications in the Tailbench benchmark suite. The results demonstrate that TailGuard can improve resource utilization by up to 80%, while meeting the targeted tail latency SLOs, as compared with the other three policies. TailGuard is also implemented and tested in a highly heterogeneous Sensing-as-a-Service (SaS) testbed for a data sensing service, with test results in line with the other ones. 

A s a c om pl e men t t o da ta d edupli cat ion , de lta c om p ress i on fu r- t he r r edu c es t h e dat a vo l u m e by c o m pr e ssi n g n o n - dup li c a t e d ata chunk s r e l a t iv e to t h e i r s i m il a r chunk s (bas e chunk s). H ow ever, ex is t i n g p o s t - d e dup li c a t i o n d e l t a c o m pr e ssi o n a p- p ro a ches fo r bac kup s t or ag e e i t h e r su ffe r f ro m t h e l ow s i m - il a r i t y b e twee n m any de te c ted c hun ks o r m i ss so me po t e n - t i a l s i m il a r c hunks , o r su ffer f r om l ow (ba ckup and r es t ore ) th r oug hpu t du e t o extr a I/ Os f or r e a d i n g b a se c hun ks o r a dd a dd i t i on a l s e r v i c e - d i s r up t ive op e r a t i on s to b a ck up s ys t em s. I n t h i s pa p e r, w e pr opo se L oop D e l t a t o a dd ress the above - m e n t i on e d prob l e m s by an e nha nced em b e ddi n g d e l t a c o m p - r e ss i on sc heme i n d e dup li c a t i on i n a non - i n t ru s ive way. T h e e nha nce d d elt a c o mpr ess ion s che m e co m b in e s f our key t e c h - ni qu e s : (1) du a l - l o c a li t y - b a s e d s i m il a r i t y t r a c k i n g to d e t ect po t e n t i a l si m il a r chun k s b y e x p l o i t i n g both l o g i c a l and ph y - s i c a l l o c a li t y, ( 2 ) l o c a li t y - a wa r e pr e f e t c h i n g to pr efe tc h ba se c hun ks to a vo i d ex t ra I/ Os fo r r e a d i n g ba s e chun ks on t h e w r i t e p at h , (3) c a che -aware fil t e r to avo i d ext r a I/Os f or b a se c hunk s on t he read p at h, a nd (4) i nver sed de l ta co mpressi on t o perf orm de lt a co mpress i o n fo r d at a chunk s t hat a re o th e r wi se f o r b i dd e n to s er ve as ba se c hunk s by r ew r i t i n g t e c hn i qu e s d e s i g n e d t o i m p r ove r es t o re pe rf o rma nc e. E x p e r i m e n t a l re su lts indi ca te t hat L oop D e l t a i ncr ea se s t he c o m pr e ss i o n r a t i o by 1 .2410 .97 t i m e s on t op of d e dup li c a - t i on , wi t hou t no t a b l y a ffe c t i n g th e ba ck up th rou ghpu t, a nd i t i m p r ove s t he res to re p er fo r m an ce b y 1.23.57 t i m e 

Sensor networks have dynamically expanded our ability to monitor and study the world. Their presence and need keep increasing, and new hardware configurations expand the range of physical stimuli that can be accurately recorded. Sensors are also no longer simply recording the data, they process it and transform into something useful before uploading to the cloud. However, building sensor networks is costly and very time consuming. It is difficult to build upon other people’s work and there are only a few open-source solutions for integrating different devices and sensing modalities. We introduce REIP, a Reconfigurable Environmental Intelligence Platform for fast sensor network prototyping. REIP’s first and most central tool, implemented in this work, is an open-source software framework, an SDK, with a flexible modular API for data collection and analysis using multiple sensing modalities. REIP is developed with the aim of being user-friendly, device-agnostic, and easily extensible, allowing for fast prototyping of heterogeneous sensor networks. Furthermore, our software framework is implemented in Python to reduce the entrance barrier for future contributions. We demonstrate the potential and versatility of REIP in real world applications, along with performance studies and benchmark REIP SDK against similar systems. 

In current infrastructure-as-a service (IaaS) cloud services, customers are charged for the usage of computing/storage resources only, but not the network resource. The difficulty lies in the fact that it is nontrivial to allocate network resource to individual customers effectively, especially for short-lived flows, in terms of both performance and cost, due to highly dynamic environments by flows generated by all customers. To tackle this challenge, in this paper, we propose an end-to-end Price-Aware Congestion Control Protocol (PACCP) for cloud services. PACCP is a network utility maximization (NUM) based optimal congestion control protocol. It supports three different classes of services (CoSes), i.e., best effort service (BE), differentiated service (DS), and minimum rate guaranteed (MRG) service. In PACCP, the desired CoS or rate allocation for a given flow is enabled by properly setting a pair of control parameters, i.e., a minimum guaranteed rate and a utility weight, which in turn, determines the price paid by the user of the flow. Two pricing models, i.e., a coarse-grained VM-Based Pricing model (VBP) and a fine-grained Flow-Based Pricing model (FBP), are proposed. The optimality of PACCP is verified by both large scale simulation and small testbed implementation. The price-performance consistency of PACCP are evaluated using real datacenter workloads. The results demonstrate that PACCP provides minimum rate guarantee, high bandwidth utilization and fair rate allocation, commensurate with the pricing models.

 

Abstract We study the ground state properties of the Hubbard model on three-leg triangular cylinders using large-scale density-matrix renormalization group simulations. At half-filling, we identify an intermediate gapless spin liquid phase, which has one gapless spin mode and algebraic spin–spin correlations but exponential decay scalar chiral–chiral correlations, between a metallic phase at weak coupling and Mott insulating dimer phase at strong interaction. Upon light doping the gapless spin liquid, the system exhibits power-law charge-density-wave (CDW) correlations but short-range single-particle, spin–spin, and chiral–chiral correlations. Similar to CDW correlations, the superconducting correlations also decay in power-law but oscillate in sign as a function of distance, which is consistent with the striped pair-density wave. When further doping the gapless spin liquid phase or doping the dimer order phase, another phase takes over, which has similar CDW correlations but all other correlations decay exponentially.