Existing　distributed　graph-processing　frameworks,　e.g.,　Pregel,　GPS　and　Giraph,　handle　large-scale　graphs　in　the　memory　of　clusters　built　of　commodity　compute　nodes　for　better　scalability　and　performance.　While　capable　of　scaling　out　according　to　the　size　of　graphs　up　to　thousands　of　compute　nodes,　for　graphs　beyond　a　certain　size,　these　frameworks　would　usually　require　investments　of　machines　that　are　either　beyond　the　financial　capability　of　or　unprofitable　for　most　small　and　medium-sized　organizations,　making　the　deployment　of　their　large-scale　graph-computing　jobs　difficult　if　not　impossible.　At　the　other　end　of　the　spectrum　of　graph-processing　frameworks　research,　the　single-node　disk-based　graph-computing　frameworks,　such　as　GraphChi　and　XStream,　handle　large-scale　graphs　on　just　one　commodity　computer,　leading　to　high　efficiency　in　the　use　of　hardware　but　at　the　cost　of　low　user　performance　and　limited　scalability.　Motivated　by　this　dichotomy,　in　this　paper　we　propose　a　pipeline-based　task　scheduling　strategy　with　high　cost-effectiveness.　We　use　this　scheduling　strategy　to　design　and　implement　a　distributed　disk-based　graph-processing　framework,　called　DD-Graph,　that　can　process　very　large　graphs　with　trillions　of　edges　on　a　small　cluster　while　achieving　the　high　performance　of　existing　distributed　in-memory　graph-processing　frameworks.　The　evaluation　of　DD-Graph　prototype,　driven　by　very　large　graph　datasets,　shows　that　it　saves　73%　of　GPS＇　hardware　costs　while　running　1.34x　faster　than　GPS.　(C)　2018　Elsevier　B.V.　All　rights　reserved.